
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import os
import warnings
warnings.filterwarnings('ignore')


os.getcwd()

'C:\\Users\\Noble'


os.chdir ('C:\\Noble\\Training\\Top Mentor\\Training\\Presentation\\Project\\Project -5 Marketting Department\\')
os.getcwd()

'C:\\Noble\\Training\\Top Mentor\\Training\\Presentation\\Project\\Project -5 Marketting Department'


creditcard_df = pd.read_csv('Marketing_data.csv')
display (creditcard_df )


creditcard_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8950 entries, 0 to 8949
Data columns (total 18 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   CUST_ID                           8950 non-null   object 
 1   BALANCE                           8950 non-null   float64
 2   BALANCE_FREQUENCY                 8950 non-null   float64
 3   PURCHASES                         8950 non-null   float64
 4   ONEOFF_PURCHASES                  8950 non-null   float64
 5   INSTALLMENTS_PURCHASES            8950 non-null   float64
 6   CASH_ADVANCE                      8950 non-null   float64
 7   PURCHASES_FREQUENCY               8950 non-null   float64
 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64
 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64
 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64
 11  CASH_ADVANCE_TRX                  8950 non-null   int64  
 12  PURCHASES_TRX                     8950 non-null   int64  
 13  CREDIT_LIMIT                      8949 non-null   float64
 14  PAYMENTS                          8950 non-null   float64
 15  MINIMUM_PAYMENTS                  8637 non-null   float64
 16  PRC_FULL_PAYMENT                  8950 non-null   float64
 17  TENURE                            8950 non-null   int64  
dtypes: float64(14), int64(3), object(1)
memory usage: 1.2+ MB


creditcard_df.describe()


# Check who made one off purchase of $40761 ie maximum ONEOFF_PURCHASES 
creditcard_df[creditcard_df['ONEOFF_PURCHASES'] == 40761.25]


creditcard_df['CASH_ADVANCE'].max()

47137.21176


# Check who made cash advance of $47137
# This customer made 123 cash advance transactions
# Never paid credit card in full
creditcard_df[creditcard_df['CASH_ADVANCE'] == 47137.211760000006]


# Check for missing Data 
creditcard_df.isnull().sum()

CUST_ID                               0
BALANCE                               0
BALANCE_FREQUENCY                     0
PURCHASES                             0
ONEOFF_PURCHASES                      0
INSTALLMENTS_PURCHASES                0
CASH_ADVANCE                          0
PURCHASES_FREQUENCY                   0
ONEOFF_PURCHASES_FREQUENCY            0
PURCHASES_INSTALLMENTS_FREQUENCY      0
CASH_ADVANCE_FREQUENCY                0
CASH_ADVANCE_TRX                      0
PURCHASES_TRX                         0
CREDIT_LIMIT                          1
PAYMENTS                              0
MINIMUM_PAYMENTS                    313
PRC_FULL_PAYMENT                      0
TENURE                                0
dtype: int64


# Heat map for missing data  
sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")

<AxesSubplot:>


# Fill up the missing elements with mean of the 'MINIMUM_PAYMENT' 
creditcard_df.loc[(creditcard_df['MINIMUM_PAYMENTS'].isnull() == True), 'MINIMUM_PAYMENTS'] = creditcard_df['MINIMUM_PAYMENTS'].mean()


# Fill up the missing elements with mean of the 'CREDIT_LIMIT' 
creditcard_df.loc[(creditcard_df['CREDIT_LIMIT'].isnull() == True), 'CREDIT_LIMIT'] = creditcard_df['CREDIT_LIMIT'].mean()


creditcard_df.duplicated().sum()

0


creditcard_df.isnull().sum()

CUST_ID                             0
BALANCE                             0
BALANCE_FREQUENCY                   0
PURCHASES                           0
ONEOFF_PURCHASES                    0
INSTALLMENTS_PURCHASES              0
CASH_ADVANCE                        0
PURCHASES_FREQUENCY                 0
ONEOFF_PURCHASES_FREQUENCY          0
PURCHASES_INSTALLMENTS_FREQUENCY    0
CASH_ADVANCE_FREQUENCY              0
CASH_ADVANCE_TRX                    0
PURCHASES_TRX                       0
CREDIT_LIMIT                        0
PAYMENTS                            0
MINIMUM_PAYMENTS                    0
PRC_FULL_PAYMENT                    0
TENURE                              0
dtype: int64


 sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")

<AxesSubplot:>


# Check for duplicated entries in the data
creditcard_df.duplicated().sum()

0


# Drop Customer ID since it has no meaning here 
creditcard_df.drop("CUST_ID", axis = 1, inplace= True)
display(creditcard_df)


n = len(creditcard_df.columns)
display(n)

17


display (creditcard_df.columns)

Index(['BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES',
       'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY',
       'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY',
       'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX',
       'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT',
       'TENURE'],
      dtype='object')


# distplot combines the matplotlib.hist function with seaborn kdeplot()
# KDE Plot represents the Kernel Density Estimate
# KDE is used for visualizing the Probability Density of a continuous variable. 
# KDE demonstrates the probability density at different values in a continuous variable. 

# Mean of balance is $1500
# 'Balance_Frequency' for most customers is updated frequently ~1
# For 'PURCHASES_FREQUENCY', there are two distinct group of customers
# For 'ONEOFF_PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY' most users don't do one off puchases or installment purchases frequently 
# Very small number of customers pay their balance in full 'PRC_FULL_PAYMENT'~0
# Credit limit average is around $4500
# Most customers are ~11 years tenure

plt.figure(figsize=(20,80))
for i in range(len(creditcard_df.columns)):
  plt.subplot(17, 1, i+1)
  sns.distplot(creditcard_df[creditcard_df.columns[i]], kde_kws={"color": "b", "lw": 3, "label": "KDE"}, hist_kws={"color": "g"})
  plt.title(creditcard_df.columns[i])

plt.tight_layout()


sns.pairplot(creditcard_df)
# Correlation between 'PURCHASES' and ONEOFF_PURCHASES & INSTALMENT_PURCHASES 
# Trend between 'PURCHASES' and 'CREDIT_LIMIT' & 'PAYMENTS'

<seaborn.axisgrid.PairGrid at 0x1aae47a67c8>


correlations = creditcard_df.corr()
display (correlations )


f, ax = plt.subplots(figsize = (20, 20))
sns.heatmap(correlations, annot = True)

# 'PURCHASES' have high correlation between one-off purchases, 'installment purchases, purchase transactions, credit limit and payments. 
# Strong Positive Correlation between 'PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY'

<AxesSubplot:>


display (creditcard_df)


# Let's scale the data first
scaler = StandardScaler()
creditcard_df_scaled = scaler.fit_transform(creditcard_df)


creditcard_df_scaled.shape

(8950, 17)


display(pd.DataFrame(creditcard_df_scaled))


scores_1 = []

range_values = range(1, 20)

for i in range_values:
  kmeans = KMeans(n_clusters = i)
  kmeans.fit(creditcard_df_scaled)
  scores_1.append(kmeans.inertia_) 

plt.plot(scores_1, 'bx-')
plt.title('Finding the right number of clusters')
plt.xlabel('Clusters')
plt.ylabel('Scores') 
plt.show()

# From this we can observe that, 4th cluster seems to be forming the elbow of the curve. 
# However, the values does not reduce linearly until 8th cluster. 
# Let's choose the number of clusters to be 7.


kmeans = KMeans(8)
kmeans.fit(creditcard_df_scaled)
labels = kmeans.labels_


kmeans.cluster_centers_.shape

(8, 17)


cluster_centers = pd.DataFrame(data = kmeans.cluster_centers_, columns = [creditcard_df.columns])
display(cluster_centers )


# In order to understand what these numbers mean, perform inverse transformation
cluster_centers = scaler.inverse_transform(cluster_centers)
cluster_centers = pd.DataFrame(data = cluster_centers, columns = [creditcard_df.columns])
display(cluster_centers)

# First Customers cluster (Transactors): Those are customers who pay least amount of intrerest charges and careful with their money, Cluster with lowest balance ($104) and cash advance ($303), Percentage of full payment = 23%
# Second customers cluster (revolvers) who use credit card as a loan (most lucrative sector): highest balance ($5000) and cash advance (~$5000), low purchase frequency, high cash advance frequency (0.5), high cash advance transactions (16) and low percentage of full payment (3%)
# Third customer cluster (VIP/Prime): high credit limit $16K and highest percentage of full payment, target for increase credit limit and increase spending habits
# Fourth customer cluster (low tenure): these are customers with low tenure (7 years), low balance


display(labels.shape) # Labels associated to each data point
display (labels.max())
display (labels.min())

(8950,)

7

0


y_kmeans = kmeans.fit_predict(creditcard_df_scaled)
display(y_kmeans)

array([0, 4, 3, ..., 7, 7, 7])


# concatenate the clusters labels to our original dataframe
creditcard_df_cluster = pd.concat([creditcard_df, pd.DataFrame({'cluster':labels})], axis = 1)
creditcard_df_cluster.head()


for i in creditcard_df.columns:
  plt.figure(figsize = (20,5))
  for j in range(8):
    plt.subplot(1,8,j+1)
    cluster = creditcard_df_cluster[creditcard_df_cluster['cluster'] == j]
    cluster[i].hist(bins = 20)
    plt.title('{}    \nCluster {} '.format(i,j))
  
  plt.show()


pca = PCA(n_components=2)
principal_comp = pca.fit_transform(creditcard_df_scaled)
display(principal_comp)

array([[-1.68222051, -1.07644994],
       [-1.13829486,  2.50647039],
       [ 0.96968244, -0.38351015],
       ...,
       [-0.92620346, -1.81078614],
       [-2.33655065, -0.65797142],
       [-0.55642139, -0.40047045]])


# Create a dataframe with the two components
pca_df = pd.DataFrame(data = principal_comp, columns =['pca1','pca2'])
pca_df.head()


# Concatenate the clusters labels to the dataframe
pca_df = pd.concat([pca_df,pd.DataFrame({'cluster':labels})], axis = 1)
display(pca_df)


pca_df.value_counts(pca_df.cluster)

cluster
2    2723
0    2014
5    1180
4    1098
1     859
6     623
3     423
7      30
dtype: int64


plt.figure(figsize=(20,10))
ax = sns.scatterplot(x="pca1", y="pca2", hue = "cluster", data = pca_df, palette =['red','green','blue','pink','yellow','gray','purple', 'black'])
plt.show()


from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, Dropout
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.initializers import glorot_uniform  # This is normalizer
from keras.optimizers import SGD

encoding_dim = 7

input_df = Input(shape=(17,)) # 17 Features 


# Glorot normal initializer (Xavier normal initializer) draws samples from a truncated normal distribution 

x = Dense(encoding_dim, activation='relu')(input_df)
x = Dense(500, activation='relu', kernel_initializer = 'glorot_uniform')(x)
x = Dense(500, activation='relu', kernel_initializer = 'glorot_uniform')(x)
x = Dense(2000, activation='relu', kernel_initializer = 'glorot_uniform')(x)

encoded = Dense(10, activation='relu', kernel_initializer = 'glorot_uniform')(x)

x = Dense(2000, activation='relu', kernel_initializer = 'glorot_uniform')(encoded)
x = Dense(500, activation='relu', kernel_initializer = 'glorot_uniform')(x)

decoded = Dense(17, kernel_initializer = 'glorot_uniform')(x)

# autoencoder
autoencoder = Model(input_df, decoded)

#encoder - used for our dimention reduction
encoder = Model(input_df, encoded)

autoencoder.compile(optimizer= 'adam', loss='mean_squared_error')


display (creditcard_df_scaled.shape)

(8950, 17)


autoencoder.fit(creditcard_df_scaled, creditcard_df_scaled, batch_size = 128, epochs = 25,  verbose = 1)

Epoch 1/25
70/70 [==============================] - 2s 35ms/step - loss: 0.6248
Epoch 2/25
70/70 [==============================] - 3s 37ms/step - loss: 0.3780
Epoch 3/25
70/70 [==============================] - 3s 39ms/step - loss: 0.3174
Epoch 4/25
70/70 [==============================] - 2s 36ms/step - loss: 0.2786
Epoch 5/25
70/70 [==============================] - 3s 38ms/step - loss: 0.2363
Epoch 6/25
70/70 [==============================] - 3s 38ms/step - loss: 0.2244
Epoch 7/25
70/70 [==============================] - 3s 36ms/step - loss: 0.2052
Epoch 8/25
70/70 [==============================] - 3s 39ms/step - loss: 0.2025
Epoch 9/25
70/70 [==============================] - 3s 37ms/step - loss: 0.1876
Epoch 10/25
70/70 [==============================] - 3s 42ms/step - loss: 0.1788
Epoch 11/25
70/70 [==============================] - 3s 43ms/step - loss: 0.1719
Epoch 12/25
70/70 [==============================] - 3s 47ms/step - loss: 0.1601
Epoch 13/25
70/70 [==============================] - 3s 47ms/step - loss: 0.1502
Epoch 14/25
70/70 [==============================] - 3s 43ms/step - loss: 0.1420
Epoch 15/25
70/70 [==============================] - 3s 38ms/step - loss: 0.1432
Epoch 16/25
70/70 [==============================] - 3s 39ms/step - loss: 0.1324
Epoch 17/25
70/70 [==============================] - 3s 42ms/step - loss: 0.1326
Epoch 18/25
70/70 [==============================] - 3s 42ms/step - loss: 0.1298
Epoch 19/25
70/70 [==============================] - 4s 54ms/step - loss: 0.1299
Epoch 20/25
70/70 [==============================] - 3s 40ms/step - loss: 0.1213
Epoch 21/25
70/70 [==============================] - 2s 35ms/step - loss: 0.1208
Epoch 22/25
70/70 [==============================] - 3s 46ms/step - loss: 0.1103
Epoch 23/25
70/70 [==============================] - 3s 45ms/step - loss: 0.1124
Epoch 24/25
70/70 [==============================] - 3s 41ms/step - loss: 0.1109
Epoch 25/25
70/70 [==============================] - 3s 45ms/step - loss: 0.1055

<tensorflow.python.keras.callbacks.History at 0x1aa82fe2a88>


autoencoder.summary()

Model: "functional_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 17)]              0         
_________________________________________________________________
dense (Dense)                (None, 7)                 126       
_________________________________________________________________
dense_1 (Dense)              (None, 500)               4000      
_________________________________________________________________
dense_2 (Dense)              (None, 500)               250500    
_________________________________________________________________
dense_3 (Dense)              (None, 2000)              1002000   
_________________________________________________________________
dense_4 (Dense)              (None, 10)                20010     
_________________________________________________________________
dense_5 (Dense)              (None, 2000)              22000     
_________________________________________________________________
dense_6 (Dense)              (None, 500)               1000500   
_________________________________________________________________
dense_7 (Dense)              (None, 17)                8517      
=================================================================
Total params: 2,307,653
Trainable params: 2,307,653
Non-trainable params: 0
_________________________________________________________________


autoencoder.save_weights('autoencoder.h5')


pred = encoder.predict(creditcard_df_scaled)
display (pd.DataFrame(pred))


pred.shape

(8950, 10)


scores_2 = []

range_values = range(1, 20)

for i in range_values:
  kmeans = KMeans(n_clusters= i)
  kmeans.fit(pred)
  scores_2.append(kmeans.inertia_)

plt.plot(scores_2, 'bx-')
plt.title('Finding right number of clusters')
plt.xlabel('Clusters')
plt.ylabel('scores') 
plt.show()


plt.plot(scores_1, 'bx-', color = 'r')
plt.plot(scores_2, 'bx-', color = 'g')

[<matplotlib.lines.Line2D at 0x1aa837213c8>]


kmeans = KMeans(4)
kmeans.fit(pred)
labels = kmeans.labels_
y_kmeans = kmeans.fit_predict(creditcard_df_scaled)


display(labels.shape) # Labels associated to each data point
display (labels.max())
display (labels.min())

(8950,)

3

0


y_kmeans = kmeans.fit_predict(pred)
display(y_kmeans)

array([1, 0, 1, ..., 0, 0, 0])


df_cluster_dr = pd.concat([creditcard_df, pd.DataFrame({'cluster':labels})], axis = 1)
display(df_cluster_dr.head())


pca = PCA(n_components=2)
prin_comp = pca.fit_transform(pred)
pca_df = pd.DataFrame(data = prin_comp, columns =['pca1','pca2'])
display (pca_df.head())


pca_df = pd.concat([pca_df,pd.DataFrame({'cluster':labels})], axis = 1)
pca_df.head()


pca_df.value_counts(pca_df.cluster)

cluster
1    5917
2    2286
0     688
3      59
dtype: int64


plt.figure(figsize=(10,10))
ax = sns.scatterplot(x="pca1", y="pca2", hue = "cluster", data = pca_df, palette =['red','green','blue','yellow'])
plt.show()

	CUST_ID	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
0	C10001	40.900749	0.818182	95.40	0.00	95.40	0.000000	0.166667	0.000000	0.083333	0.000000	0	2	1000.0	201.802084	139.509787	0.000000	12
1	C10002	3202.467416	0.909091	0.00	0.00	0.00	6442.945483	0.000000	0.000000	0.000000	0.250000	4	0	7000.0	4103.032597	1072.340217	0.222222	12
2	C10003	2495.148862	1.000000	773.17	773.17	0.00	0.000000	1.000000	1.000000	0.000000	0.000000	0	12	7500.0	622.066742	627.284787	0.000000	12
3	C10004	1666.670542	0.636364	1499.00	1499.00	0.00	205.788017	0.083333	0.083333	0.000000	0.083333	1	1	7500.0	0.000000	NaN	0.000000	12
4	C10005	817.714335	1.000000	16.00	16.00	0.00	0.000000	0.083333	0.083333	0.000000	0.000000	0	1	1200.0	678.334763	244.791237	0.000000	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8945	C19186	28.493517	1.000000	291.12	0.00	291.12	0.000000	1.000000	0.000000	0.833333	0.000000	0	6	1000.0	325.594462	48.886365	0.500000	6
8946	C19187	19.183215	1.000000	300.00	0.00	300.00	0.000000	1.000000	0.000000	0.833333	0.000000	0	6	1000.0	275.861322	NaN	0.000000	6
8947	C19188	23.398673	0.833333	144.40	0.00	144.40	0.000000	0.833333	0.000000	0.666667	0.000000	0	5	1000.0	81.270775	82.418369	0.250000	6
8948	C19189	13.457564	0.833333	0.00	0.00	0.00	36.558778	0.000000	0.000000	0.000000	0.166667	2	0	500.0	52.549959	55.755628	0.250000	6
8949	C19190	372.708075	0.666667	1093.25	1093.25	0.00	127.040008	0.666667	0.666667	0.000000	0.333333	2	23	1200.0	63.165404	88.288956	0.000000	6

	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
count	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8950.000000	8949.000000	8950.000000	8637.000000	8950.000000	8950.000000
mean	1564.474828	0.877271	1003.204834	592.437371	411.067645	978.871112	0.490351	0.202458	0.364437	0.135144	3.248827	14.709832	4494.449450	1733.143852	864.206542	0.153715	11.517318
std	2081.531879	0.236904	2136.634782	1659.887917	904.338115	2097.163877	0.401371	0.298336	0.397448	0.200121	6.824647	24.857649	3638.815725	2895.063757	2372.446607	0.292499	1.338331
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	50.000000	0.000000	0.019163	0.000000	6.000000
25%	128.281915	0.888889	39.635000	0.000000	0.000000	0.000000	0.083333	0.000000	0.000000	0.000000	0.000000	1.000000	1600.000000	383.276166	169.123707	0.000000	12.000000
50%	873.385231	1.000000	361.280000	38.000000	89.000000	0.000000	0.500000	0.083333	0.166667	0.000000	0.000000	7.000000	3000.000000	856.901546	312.343947	0.000000	12.000000
75%	2054.140036	1.000000	1110.130000	577.405000	468.637500	1113.821139	0.916667	0.300000	0.750000	0.222222	4.000000	17.000000	6500.000000	1901.134317	825.485459	0.142857	12.000000
max	19043.138560	1.000000	49039.570000	40761.250000	22500.000000	47137.211760	1.000000	1.000000	1.000000	1.500000	123.000000	358.000000	30000.000000	50721.483360	76406.207520	1.000000	12.000000

	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
0	40.900749	0.818182	95.40	0.00	95.40	0.000000	0.166667	0.000000	0.083333	0.000000	0	2	1000.0	201.802084	139.509787	0.000000	12
1	3202.467416	0.909091	0.00	0.00	0.00	6442.945483	0.000000	0.000000	0.000000	0.250000	4	0	7000.0	4103.032597	1072.340217	0.222222	12
2	2495.148862	1.000000	773.17	773.17	0.00	0.000000	1.000000	1.000000	0.000000	0.000000	0	12	7500.0	622.066742	627.284787	0.000000	12
3	1666.670542	0.636364	1499.00	1499.00	0.00	205.788017	0.083333	0.083333	0.000000	0.083333	1	1	7500.0	0.000000	864.206542	0.000000	12
4	817.714335	1.000000	16.00	16.00	0.00	0.000000	0.083333	0.083333	0.000000	0.000000	0	1	1200.0	678.334763	244.791237	0.000000	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8945	28.493517	1.000000	291.12	0.00	291.12	0.000000	1.000000	0.000000	0.833333	0.000000	0	6	1000.0	325.594462	48.886365	0.500000	6
8946	19.183215	1.000000	300.00	0.00	300.00	0.000000	1.000000	0.000000	0.833333	0.000000	0	6	1000.0	275.861322	864.206542	0.000000	6
8947	23.398673	0.833333	144.40	0.00	144.40	0.000000	0.833333	0.000000	0.666667	0.000000	0	5	1000.0	81.270775	82.418369	0.250000	6
8948	13.457564	0.833333	0.00	0.00	0.00	36.558778	0.000000	0.000000	0.000000	0.166667	2	0	500.0	52.549959	55.755628	0.250000	6
8949	372.708075	0.666667	1093.25	1093.25	0.00	127.040008	0.666667	0.666667	0.000000	0.333333	2	23	1200.0	63.165404	88.288956	0.000000	6

	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
BALANCE	1.000000	0.322412	0.181261	0.164350	0.126469	0.496692	-0.077944	0.073166	-0.063186	0.449218	0.385152	0.154338	0.531267	0.322802	0.394282	-0.318959	0.072692
BALANCE_FREQUENCY	0.322412	1.000000	0.133674	0.104323	0.124292	0.099388	0.229715	0.202415	0.176079	0.191873	0.141555	0.189626	0.095795	0.065008	0.114249	-0.095082	0.119776
PURCHASES	0.181261	0.133674	1.000000	0.916845	0.679896	-0.051474	0.393017	0.498430	0.315567	-0.120143	-0.067175	0.689561	0.356959	0.603264	0.093515	0.180379	0.086288
ONEOFF_PURCHASES	0.164350	0.104323	0.916845	1.000000	0.330622	-0.031326	0.264937	0.524891	0.127729	-0.082628	-0.046212	0.545523	0.319721	0.567292	0.048597	0.132763	0.064150
INSTALLMENTS_PURCHASES	0.126469	0.124292	0.679896	0.330622	1.000000	-0.064244	0.442418	0.214042	0.511351	-0.132318	-0.073999	0.628108	0.256496	0.384084	0.131687	0.182569	0.086143
CASH_ADVANCE	0.496692	0.099388	-0.051474	-0.031326	-0.064244	1.000000	-0.215507	-0.086754	-0.177070	0.628522	0.656498	-0.075850	0.303983	0.453238	0.139223	-0.152935	-0.068312
PURCHASES_FREQUENCY	-0.077944	0.229715	0.393017	0.264937	0.442418	-0.215507	1.000000	0.501343	0.862934	-0.308478	-0.203478	0.568430	0.119778	0.103464	0.002976	0.305802	0.061506
ONEOFF_PURCHASES_FREQUENCY	0.073166	0.202415	0.498430	0.524891	0.214042	-0.086754	0.501343	1.000000	0.142329	-0.111716	-0.069088	0.544869	0.295030	0.243537	-0.029963	0.157531	0.082466
PURCHASES_INSTALLMENTS_FREQUENCY	-0.063186	0.176079	0.315567	0.127729	0.511351	-0.177070	0.862934	0.142329	1.000000	-0.262958	-0.169207	0.529975	0.060752	0.085551	0.029590	0.250087	0.073275
CASH_ADVANCE_FREQUENCY	0.449218	0.191873	-0.120143	-0.082628	-0.132318	0.628522	-0.308478	-0.111716	-0.262958	1.000000	0.799561	-0.131168	0.132616	0.183192	0.097898	-0.249773	-0.133372
CASH_ADVANCE_TRX	0.385152	0.141555	-0.067175	-0.046212	-0.073999	0.656498	-0.203478	-0.069088	-0.169207	0.799561	1.000000	-0.066157	0.149699	0.255278	0.109185	-0.169784	-0.043421
PURCHASES_TRX	0.154338	0.189626	0.689561	0.545523	0.628108	-0.075850	0.568430	0.544869	0.529975	-0.131168	-0.066157	1.000000	0.272877	0.370832	0.095858	0.162066	0.121874
CREDIT_LIMIT	0.531267	0.095795	0.356959	0.319721	0.256496	0.303983	0.119778	0.295030	0.060752	0.132616	0.149699	0.272877	1.000000	0.421852	0.125134	0.055671	0.139034
PAYMENTS	0.322802	0.065008	0.603264	0.567292	0.384084	0.453238	0.103464	0.243537	0.085551	0.183192	0.255278	0.370832	0.421852	1.000000	0.125046	0.112138	0.106136
MINIMUM_PAYMENTS	0.394282	0.114249	0.093515	0.048597	0.131687	0.139223	0.002976	-0.029963	0.029590	0.097898	0.109185	0.095858	0.125134	0.125046	1.000000	-0.139674	0.057257
PRC_FULL_PAYMENT	-0.318959	-0.095082	0.180379	0.132763	0.182569	-0.152935	0.305802	0.157531	0.250087	-0.249773	-0.169784	0.162066	0.055671	0.112138	-0.139674	1.000000	-0.016486
TENURE	0.072692	0.119776	0.086288	0.064150	0.086143	-0.068312	0.061506	0.082466	0.073275	-0.133372	-0.043421	0.121874	0.139034	0.106136	0.057257	-0.016486	1.000000

	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
0	40.900749	0.818182	95.40	0.00	95.40	0.000000	0.166667	0.000000	0.083333	0.000000	0	2	1000.0	201.802084	139.509787	0.000000	12
1	3202.467416	0.909091	0.00	0.00	0.00	6442.945483	0.000000	0.000000	0.000000	0.250000	4	0	7000.0	4103.032597	1072.340217	0.222222	12
2	2495.148862	1.000000	773.17	773.17	0.00	0.000000	1.000000	1.000000	0.000000	0.000000	0	12	7500.0	622.066742	627.284787	0.000000	12
3	1666.670542	0.636364	1499.00	1499.00	0.00	205.788017	0.083333	0.083333	0.000000	0.083333	1	1	7500.0	0.000000	864.206542	0.000000	12
4	817.714335	1.000000	16.00	16.00	0.00	0.000000	0.083333	0.083333	0.000000	0.000000	0	1	1200.0	678.334763	244.791237	0.000000	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8945	28.493517	1.000000	291.12	0.00	291.12	0.000000	1.000000	0.000000	0.833333	0.000000	0	6	1000.0	325.594462	48.886365	0.500000	6
8946	19.183215	1.000000	300.00	0.00	300.00	0.000000	1.000000	0.000000	0.833333	0.000000	0	6	1000.0	275.861322	864.206542	0.000000	6
8947	23.398673	0.833333	144.40	0.00	144.40	0.000000	0.833333	0.000000	0.666667	0.000000	0	5	1000.0	81.270775	82.418369	0.250000	6
8948	13.457564	0.833333	0.00	0.00	0.00	36.558778	0.000000	0.000000	0.000000	0.166667	2	0	500.0	52.549959	55.755628	0.250000	6
8949	372.708075	0.666667	1093.25	1093.25	0.00	127.040008	0.666667	0.666667	0.000000	0.333333	2	23	1200.0	63.165404	88.288956	0.000000	6

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
0	-0.731989	-0.249434	-0.424900	-0.356934	-0.349079	-0.466786	-0.806490	-0.678661	-0.707313	-0.675349	-0.476070	-0.511333	-0.960433	-0.528979	-3.109675e-01	-0.525551	0.360680
1	0.786961	0.134325	-0.469552	-0.356934	-0.454576	2.605605	-1.221758	-0.678661	-0.916995	0.573963	0.110074	-0.591796	0.688639	0.818642	8.931021e-02	0.234227	0.360680
2	0.447135	0.518084	-0.107668	0.108889	-0.454576	-0.466786	1.269843	2.673451	-0.916995	-0.675349	-0.476070	-0.109020	0.826062	-0.383805	-1.016632e-01	-0.525551	0.360680
3	0.049099	-1.016953	0.232058	0.546189	-0.454576	-0.368653	-1.014125	-0.399319	-0.916995	-0.258913	-0.329534	-0.551565	0.826062	-0.598688	-4.390474e-16	-0.525551	0.360680
4	-0.358775	0.518084	-0.462063	-0.347294	-0.454576	-0.466786	-1.014125	-0.399319	-0.916995	-0.675349	-0.476070	-0.551565	-0.905464	-0.364368	-2.657913e-01	-0.525551	0.360680
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8945	-0.737950	0.518084	-0.333293	-0.356934	-0.132643	-0.466786	1.269843	-0.678661	1.179833	-0.675349	-0.476070	-0.350408	-0.960433	-0.486217	-3.498541e-01	1.183951	-4.122768
8946	-0.742423	0.518084	-0.329136	-0.356934	-0.122823	-0.466786	1.269843	-0.678661	1.179833	-0.675349	-0.476070	-0.350408	-0.960433	-0.503396	-4.390474e-16	-0.525551	-4.122768
8947	-0.740398	-0.185477	-0.401965	-0.356934	-0.294893	-0.466786	0.854576	-0.678661	0.760469	-0.675349	-0.476070	-0.390639	-0.960433	-0.570615	-3.354655e-01	0.329200	-4.122768
8948	-0.745174	-0.185477	-0.469552	-0.356934	-0.454576	-0.449352	-1.221758	-0.678661	-0.916995	0.157527	-0.182998	-0.591796	-1.097856	-0.580536	-3.469065e-01	0.329200	-4.122768
8949	-0.572575	-0.889033	0.042146	0.301732	-0.454576	-0.406205	0.439310	1.556082	-0.916995	0.990398	-0.182998	0.333524	-0.905464	-0.576869	-3.329464e-01	-0.525551	-4.122768

	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
0	-0.365376	0.333430	-0.037549	-0.244521	0.360254	-0.365004	0.991217	-0.387760	1.206940	-0.476073	-0.361839	0.187641	-0.260776	-0.217535	-0.032678	0.314258	0.257535
1	1.699636	0.393399	-0.216668	-0.155678	-0.226370	2.026796	-0.472754	-0.211581	-0.410279	1.921920	1.942711	-0.264747	1.038660	0.828056	0.558046	-0.392175	0.071004
2	0.018988	0.404056	-0.362160	-0.247481	-0.401545	-0.085558	-0.867645	-0.411249	-0.758577	0.116672	-0.019935	-0.487257	-0.306270	-0.247787	-0.008337	-0.456223	0.272010
3	0.901817	0.466986	2.270963	1.756936	2.141920	-0.195512	1.158629	1.583889	1.226198	-0.312181	-0.212097	2.781452	1.238947	1.290295	0.441655	0.304778	0.334182
4	-0.161258	0.391841	0.453685	0.593268	-0.017360	-0.332048	0.941220	1.874193	0.089564	-0.406121	-0.322194	0.524385	0.377442	0.087496	-0.161830	0.403800	0.260639
5	-0.701210	-2.140684	-0.310913	-0.235462	-0.302449	-0.322400	-0.555391	-0.442821	-0.439779	-0.521503	-0.376311	-0.419459	-0.178034	-0.202124	-0.256992	0.281130	0.201867
6	-0.336717	-0.351618	-0.289556	-0.216192	-0.287105	0.068131	-0.204713	-0.289287	-0.225660	0.308086	0.000466	-0.388444	-0.566467	-0.392990	-0.208809	0.013145	-3.202809
7	1.923051	0.337717	11.212042	10.600367	7.033118	0.419625	1.046983	1.915501	0.981334	-0.258912	0.061229	5.362438	3.044064	8.098975	1.120318	1.110132	0.310863

	BALANCE	BALANCE_FREQUENCY	PURCHASES	ONEOFF_PURCHASES	INSTALLMENTS_PURCHASES	CASH_ADVANCE	PURCHASES_FREQUENCY	ONEOFF_PURCHASES_FREQUENCY	PURCHASES_INSTALLMENTS_FREQUENCY	CASH_ADVANCE_FREQUENCY	CASH_ADVANCE_TRX	PURCHASES_TRX	CREDIT_LIMIT	PAYMENTS	MINIMUM_PAYMENTS	PRC_FULL_PAYMENT	TENURE
0	803.975428	0.956257	922.979916	186.582195	736.841256	213.439746	0.888174	0.086781	0.844106	0.039877	0.779543	19.373883	3545.640617	1103.400546	788.052010	0.245630	11.861966
1	5102.122809	0.970463	540.289825	334.043981	206.364203	5229.157217	0.300611	0.139339	0.201382	0.519740	16.506403	8.129220	8273.521008	4130.284459	2164.709397	0.039010	11.612340
2	1603.997209	0.972988	229.443586	181.670169	47.955224	799.452462	0.142123	0.079774	0.062959	0.158491	3.112785	2.598457	3380.112662	1015.823684	844.777563	0.020277	11.881337
3	3441.530986	0.987896	5855.151608	3508.591111	2347.978936	568.874079	0.955365	0.674962	0.851760	0.072674	1.801418	83.846336	9002.245863	5468.421612	1893.464949	0.242857	11.964539
4	1228.829033	0.970094	1972.510118	1577.141330	395.368789	282.550045	0.868108	0.761566	0.400032	0.053875	1.050091	27.744080	5867.738864	1986.435913	487.069414	0.271819	11.866120
5	104.964599	0.370162	338.934708	201.619102	137.566918	302.783168	0.267445	0.070355	0.189658	0.030786	0.680779	4.283658	3846.688776	1148.014996	265.297719	0.235940	11.787468
6	863.627544	0.793976	384.563451	233.602408	151.442584	1121.745122	0.408189	0.116158	0.274754	0.196795	3.252006	5.054575	2433.410031	595.475430	377.585275	0.157559	7.231140
7	5567.142164	0.957273	24957.905000	18186.875667	6771.029333	1858.844605	0.910556	0.773889	0.754444	0.083333	3.666667	148.000000	15570.000000	25178.882690	3475.059479	0.478409	11.933333

	pca1	pca2
0	-1.682221	-1.076450
1	-1.138295	2.506470
2	0.969682	-0.383510
3	-0.873629	0.043178
4	-1.599433	-0.688584

	0	1	2	3	4	5	6	7	8	9
0	0.0	0.602049	0.0	0.0	1.051515	0.0	0.493410	0.0	1.375802	0.0
1	0.0	3.542061	0.0	0.0	2.668556	0.0	2.139897	0.0	4.230911	0.0
2	0.0	0.223179	0.0	0.0	0.096884	0.0	2.054518	0.0	1.554061	0.0
3	0.0	0.590291	0.0	0.0	1.160528	0.0	1.726851	0.0	2.800796	0.0
4	0.0	0.518108	0.0	0.0	0.462901	0.0	0.492184	0.0	1.510843	0.0
...	...	...	...	...	...	...	...	...	...	...
8945	0.0	2.094757	0.0	0.0	4.255546	0.0	0.004677	0.0	0.698731	0.0
8946	0.0	2.724187	0.0	0.0	4.460185	0.0	0.197827	0.0	0.991715	0.0
8947	0.0	2.195770	0.0	0.0	4.383982	0.0	0.241642	0.0	1.309702	0.0
8948	0.0	1.542000	0.0	0.0	2.577138	0.0	0.000000	0.0	3.903007	0.0
8949	0.0	2.664088	0.0	0.0	3.309399	0.0	0.000000	0.0	2.906634	0.0

	pca1	pca2
0	-1.685538	-0.231119
1	2.888172	-1.278491
2	-1.549817	0.047325
3	-0.455825	-0.489197
4	-1.944900	-0.593339